Automatic estimation of current dipoles from biomagnetic data is still a problematic task. This is due not only to the ill-posedness of the inverse problem but also to two intrinsic difficulties introduced by the dipolar model: the unknown number of sources and the nonlinear relationship between the source locations and the data. Recently, we have developed a new Bayesian approach, particle filtering, based on dynamical tracking of the dipole constellation. Contrary to many dipole-based methods, particle filtering does not assume stationarity of the source configuration: the number of dipoles and their positions are estimated and updated dynamically during the course of the MEG sequence. We have now developed a Matlab-based graphical user interface, which allows nonexpert users to do automatic dipole estimation from MEG data with particle filtering. In the present paper, we describe the main features of the software and show the analysis of both a synthetic data set and an experimental dataset
Highly Automated Dipole EStimation (HADES) / C. Campi; A. Pascarella; A. Sorrentino; M. Piana. - In: COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE. - ISSN 1687-5265. - ELETTRONICO. - 2011(2011), pp. 982185-982185.
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|Titolo:||Highly Automated Dipole EStimation (HADES)|
|Data di pubblicazione:||2011|
|Citazione:||Highly Automated Dipole EStimation (HADES) / C. Campi; A. Pascarella; A. Sorrentino; M. Piana. - In: COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE. - ISSN 1687-5265. - ELETTRONICO. - 2011(2011), pp. 982185-982185.|
|Appare nelle tipologie:||01.01 - Articolo su rivista|